Failed renders, temporal artifacts, broken anatomy, flicker, model-loading errors, unstable workflows, and systematic root-cause analysis.
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This post analyzes a common Wan2.2 failure mode where a render looks great for eleven frames before shifting clothing identity and style. By examining frame counts, VRAM pressure, and debugging steps, it explores how to maintain temporal consistency and why documenting limits on consumer hardware is essential for reliable AI video workflows.
This post details a twenty-run debugging session that uncovered a subtle VAE failure mode during Wan2.2 video generation. By isolating the decode path from the sampler, the author demonstrates how minor VAE differences cause progressive softening and hue drift, proving that the VAE is a critical, measurable variable in reproducible AI video workflows.